针对大型钢结构采用单一无损检测方法经常难以实现被检测对象的完整准确评 估问题，利用多种无损检测方法进行综合检测是有效手段，其中综合检测结果可信度评定 是关键． 首先，设计基于 Hadoop 的大型钢结构无损云检测系统架构，分析大型钢结构无损 云检测 Hadoop 架构的信息流; 接着定义大型钢结构无损云检测数据可信度融合的 D-S 证 据理论联合算子，提出基于 D-S 证据理论的多源数据可信度 MapＲeduce 融合算法; 最后， 建立大型钢管塔结构检测实验平台，对可视化内窥检测、涡流检测和超声波检测等多源数 据可信度进行了融合评估实验． 结果表明，云检测数据可信度 MapＲeduce 融合算法有效 提高了各个单一检测手段的缺陷检出率，可满足大型钢结构无损云检测的实际需求．

For the problem that single testing method is difficult to achieve comprehensive assessment of the object，the comprehensive testing using a variety of nondestructive testing methods is suggested to be effective，in which the credibility assessment of comprehensive test results is critical． In the investigation，firstly，the cloud nondestructive testing system architecture for large-scale steel structures was designed based on Hadoop，and the information flow of large-scale steel structures cloud nondestructive testing Hadoop architecture was analyzed． Then，the D-S evidence theory joint operator of credibility fusion of large-scale steel structures cloud nondestructive testing data was defined，and the multi-source data credibility MapＲeduce fusion algorithm based on D-S evidence theory was proposed． Finally，a large steel tube tower structure testing experimental platform was established，and the credibility of visual endoscopic inspection，eddy current testing and conventional ultrasonic inspection multisource data was tested by fusion evaluation． The results show that the detection rate of defects in each single testing method is improved by the MapReduce fusion algorithm for the credibility of cloud testing data． It meets the cloud nondestructive testing requirements of large-scale steel structures